A researcher from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed an obstacle-detection system that allows a drone to autonomously dip, dart and dive through a tree-filled field at upwards of 30 miles per hour.
CSAIL PhD student Andrew Barry developed the system as part of his thesis with MIT professor Russ Tedrake.
How the Self Flying drone works?
Ideally, a self flying drone – or any flying object for that matter, has to navigate through obstacles without secondary assistance. Traditional algorithms focused on this problem would use the images captured by each camera, and search through the depth-field at multiple distances – 1 meter, 2 meters, 3 meters, and so on – to determine if an object is in the drone’s path.
Such approaches, however, are computationally intensive, meaning that the drone cannot fly any faster than 5 or 6 miles per hour without specialized processing hardware.
As CSAIL reports, Barry’s algorithm work with a 10-meter horizon. As the drone moves, the navigational map is updated accordingly.
While such a method might seem limiting, the software can quickly recover the missing depth information by integrating results from the drone’s odometry and previous distances.
Barry says that he hopes to further improve the algorithms so that they can work at more than one depth, and in environments as dense as a thick forest.
Running 20 times faster than existing software, Barry’s stereo-vision algorithm allows the drone to detect objects and build a full map of its surroundings in real-time. Operating at 120 frames per second, the software – which is open-source and available online – extracts depth information at a speed of 8.3 milliseconds per frame.
The drone, which weighs just over a pound and has a 34-inch wingspan, was made from off-the-shelf components costing about $1,700, including a camera on each wing and two processors similar to a Samsung Galaxy S3.